Personal Health Baseline Tracker

3.30

Derivation Chain

Step 1 Family-shared medical records & Health Checkup history integration

→

Step 2 Difficulty interpreting checkup results

→

Step 3 Tracking changes against personal normal ranges

Problem

Health Checkup results show 'normal ranges,' but these are population-wide standards. If a man in his 50s sees his fasting blood sugar climb by 5 points each year to reach 99 (within 'normal range'), that's actually a warning sign—but current checkup systems don't flag it. Comparing past results requires manually pulling out PDFs and transferring data to a spreadsheet.

Solution

Users input past Health Checkup results by year (PDF upload with OCR auto-extraction), and the tool visualizes personal trends for each metric (blood sugar, blood pressure, cholesterol, liver enzymes, etc.) as graphs. It automatically detects metrics that have sharply deviated from the user's personal baseline, providing personalized alerts like 'This metric has been rising for 3 consecutive years → internal medicine consultation recommended.'

Target: Adults aged 45-60 who get annual national Health Checkups and wonder 'what changed since last year?' but have no easy way to compare

Revenue Model: 3-metric trend analysis is Free. Full metric (20+) trend analysis + alerts at Annual Subscription of 9,900 KRW (~$7.40)/year. Health management app partnerships.

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

4.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (72%)

Tech Complexity

29.3/40

Data Availability

23.1/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (53/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

9.0/15

Pick-Axe Fit

9.0/15

Solo Buildability

7.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Frontend [medium] Backend [medium] AI/ML [low]

Dashboard